Uncovering Genetic Risk Beyond Diagnoses in Suicidal Thoughts and Behaviors: Insights from All of Us
Lee, P. H.; Sanzo, B. T.; Lee, Y. H.; Jung, D. H.; Gilman, J. H.; Nock, M. K.; Smoller, J. W.; Liu, R. T.; Kessler, R. C.
Show abstract
STRUCTURED ABSTRACTO_ST_ABSImportanceC_ST_ABSSuicide is a leading cause of death worldwide, yet risk prediction remains imprecise. While psychiatric disorders are strongly associated with suicide-related outcomes, most individuals with these conditions never exhibit suicidal behaviors. Polygenic risk scores (PRSs) may help identify additional vulnerability factors beyond clinical diagnoses. ObjectiveTo evaluate the independent and interactive effects of polygenic risk for psychiatric disorders and clinical diagnoses on suicidal ideation (SI) and suicide attempts (SA) in a large, ancestrally diverse cohort. DesignCross-sectional analysis of genetic and survey data from the All of Us Research Program. SettingPopulation-based cohort study leveraging a diverse U.S. sample. Participants41,379 adults with genetic data and self-reported psychiatric diagnoses, SI, and SA. Main Outcomes and MeasuresLifetime SI and SA, assessed via self-reported surveys. Predictors included lifetime psychiatric diagnoses on 13 categories and PRSs for depression, bipolar disorder, and PTSD, derived from multi-ancestry genome-wide association studies. Ancestry-stratified multinomial logistic regression analyses were performed for African, Admixed Hispanic/Latino, and European American groups, followed by fixed-effects meta-analysis, adjusting for age, sex at birth, and socioeconomic factors. ResultsAmong 41,379 participants, 28.5% reported SI, and 12.6% reported SA. All psychiatric disorders were significantly associated with both outcomes, with depression, bipolar disorder, and PTSD showing the strongest independent effects (ORs=2.81-7.73 for SA, 1.62-3.32 for SI, all FDR < 0.05). Each additional psychiatric diagnosis more than doubled the odds of SA (OR=2.16 95% CI: 2.10-2.21). PRSs for depression, bipolar disorder, and PTSD remained significantly associated with SI and SA after adjusting for clinical diagnoses and sociodemographic covariates. For SA, depression PRS showed the strongest association (OR=1.36 [1.30-1.41], p=1.42x10-55), followed by PTSD (OR=1.33 [1.28-1.39], p=6.91x10-45) and bipolar disorder (OR=1.18 [1.13-1.23], p=1.41x10-16). Effect sizes were comparable among individuals with and without clinical diagnoses, suggesting transdiagnostic relevance. ConclusionsPolygenic risk for psychiatric disorders showed modest but significant associations with SI and SA, independent of clinical diagnoses and sociodemographic factors. These findings highlight the value of genetic information in identifying vulnerability not fully captured by diagnostic categories and underscore the importance of multi-dimensional approaches to suicide risk assessment across diverse populations. KEY POINTSO_ST_ABSQuestionC_ST_ABSDo polygenic risk scores (PRS) for psychiatric disorders independently predict suicidal ideation (SI) and suicide attempts (SA) beyond clinical diagnoses? FindingsIn 41,379 All of Us participants, socioeconomic adversity and psychiatric diagnoses were strongly associated with SI and SA. PRSs for depression, bipolar disorder, and post-traumatic stress disorder (PTSD) showed significant and independent associations with SI and SA. These associations remained regardless of clinical diagnoses, suggesting genetic risk reflects vulnerability not fully captured by diagnostic categories. MeaningWhile PRSs have limited predictive value individually, integrating genetic, clinical, and socioeconomic factors may enhance understanding of suicide risk and improve risk assessment.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Functional and molecular characterization of suicidality factors using phenotypic and genome-wide data 97%
- Correlates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder 96%
- Cross-phenotype relationship between opioid use disorder and suicide attempts: new evidence from polygenic association and Mendelian randomization analyses 95%
Similar papers in this journal
- Distinguishing clinical and genetic risk factors for suicidal ideation and behavior in a diverse hospital population 97%
- Educational attainment reduces the risk of suicide attempt among individuals with and without psychiatric disorders independent of cognition: a multivariable Mendelian randomization study with more than 815,000 participants 96%
- Are psychiatric disorders risk factors for COVID-19 susceptibility and severity? a two-sample, bidirectional, univariable and multivariable Mendelian Randomization study 93%
Similar papers in this journal
- Prevalence, Comorbidity, and Sociodemographic Correlates of Psychiatric Disorders in the All Of Us Biobank 95%
- Effect of everyday discrimination on depression and suicidal ideation during the COVID-19 pandemic: a large-scale, repeated-measures study in the All of Us Research Program 94%
- Decoding Treatment Choice: Genetic and Phenotypic Analyses of Long-term Antidepressant Acceptability 92%
Similar papers in this journal
- Trends in Prevalence of Cannabis Use Disorders among U.S. Veterans with and without Psychiatric Disorders Between 2005 and 2019 93%
- Decoding shared versus divergent transcriptomic signatures across cortico-amygdala circuitry in PTSD and depressive disorders 92%
- Pervasively thinner neocortex as a transdiagnostic feature of general psychopathology 91%
Similar papers in this journal
- Life-years lost associated with mental illness: a cohort study of beneficiaries of a South African medical insurance scheme 94%
- Father absence and trajectories of offspring mental health across adolescence and young adulthood: findings from a UK-birth cohort 92%
- Which traits predict elevated distress during the Covid-19 pandemic? Results from a large, longitudinal cohort study with psychiatric patients and healthy controls 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.